Data-driven discovery of electrocatalysts for CO2 reduction using active motifs-based machine learning

The electrochemical carbon dioxide reduction reaction (CO2RR) is an attractive approach for mitigating CO2 emissions and generating value-added products. Consequently, discovery of promising CO2RR catalysts has become a crucial task, and machine learning (ML) has been utilized to accelerate catalyst discovery. However, current ML approaches are limited to exploring narrow chemical spaces and provide only fragmentary catalytic activity, even though CO2RR produces various chemicals. Here, by merging pre-developed ML model and a CO2RR selectivity map, we establish high-throughput virtual screening strategy to suggest active and selective catalysts for CO2RR without being limited to a database. Further, this strategy can provide guidance on stoichiometry and morphology of the catalyst to researchers. We predict the activity and selectivity of 465 metallic catalysts toward four expected reaction products. During this process, we discover previously unreported and promising behavior of Cu-Ga and Cu-Pd alloys. These findings are then validated through experimental methods.


Supplementary Note 1: DSTAR-based High-throughput Virtual Screening Pipeline
As shown in Figure S1, DSTAR-based high-throughput screening begins by (i) converting all surfaces in the dataset into active motif-based representations, which are then used as fingerprints to train regression models.The representation includes positional information of the active motif, which is divided into three sites: the first nearest neighbor (FNN) atoms of the adsorbates, the second nearest neighbor atoms in the surface layer (SNNsame), and the sublayer (SNNsub).Then, it incorporates their weighted average elemental properties (Atomic number, Block, Ionic radius, Oxidation state, Electronegativity, Row, Group, Thermal conductivity, Boiling point, Melting point, First ionization energy) and the number of atoms of each site.(ii) The regression model is trained using the active motif representations and their corresponding binding energy.(iii) All active motif representations are converted into prototypes of active motifs that only contains positional information without any elemental details.This is to remove duplicates and collect unique active motifs.(iv) To expand the chemical space, new elemental combinations that are outside the original domain are inserted into the prototype of unique active motifs, resulting in a new set of active motif representations.The number of active motifs generated per elemental combination is the same as the number of prototypes of unique active motifs.(v) The trained regression model predicts the binding energies of the new set of active motifs.The predicted binding energies are plotted on a 3D selectivity map to calculate the product selectivity of a specific elemental combination.

Supplementary Note 2: Reaction Mechanism of CO 2 RR
The initial protonation step of CO2 reduction can proceed through two pathways; formation of an O-H bond to produce COOH* (reaction (i)) and formation of a C-H bond to produce HCOO* species (reaction (ii)).

CO2 (g) + H
Reaction (i) results in the production of CO*/CO (g), which is a key intermediate that can be further reduced to produce C1 or C2+ products, while reaction (ii) typically leads to the production of HCOOH production.We note that reaction (ii) is a chemical step which requires a prior surface protonation stp (reaction (iii)), so-called the Volmer step.
We will also consider the possibility of surface poisoning by OH* when the binding energy of OH* is strong enough to inhibit its protonation to water (reaction (iv)).
OH* + H + + e − + * → H2O + * (iv) The boundary conditions for the competition between HCOO* vs COOH* pathways, as well as the two additional reactions that determine surface adsorption states, can be expressed by following equations: After the production of CO* following the COOH* pathway, protonation of CO* can proceed to two possible intermediates, either CHO* or COH*.In this study, we referred to the results of energetic and kinetic investigations on both CO* protonation pathways performed by For the further reduction of COH* toward C1+ products, the protonation of CO* to form COH* is known to be the rate-determining step 2 .Thus, we used the reaction Gibbs energy of CO* protonation as a boundary condition to determine its further protonation.The criterion was set as follows.
Δ !"# (') = 0.75 eV (4) The reaction Gibbs energy was used as the criterion instead of the activation energy, since the former was found be slightly lower than the latter for Cu (100), and much more efficient to calculate. 1 To establish boundary conditions for CO2RR against hydrogen evolution reaction (HER), we refer to the theoretical results by The thermodynamic boundary conditions are represented by three binding energy descriptors (∆ECO*, ∆EH* and ∆EOH*) using linear scaling relationships between these descriptors and other intermediates of CO2RR.We note that the applied potential term, eU, in boundary conditions demonstrates its potential-dependency.The details of the scaling lines, free energy diagram, boundary conditions represented by energetic descriptors and applied potential are enumerated Figure S3, Figure S4 and Table S3, respectively.Prior to CO2RR measurements, the Cu-Ga electrode was subjected to a pre-reduction at -0.6 V in 0.1 M Ar-saturated CsHCO3 for 1200 s toward a stable surface state (Figure S14).Typical SEM images in Figure S15a and S15b depict the morphology of Cu-Ga catalyst before and after CO2RR electrolysis, respectively.In contrast to the relatively flat surface of polished Cu (Figure S16), the electroplated Cu-Ga consists of densely packed nanoparticles, which contributes to the 2.67-times higher surface roughness compared to bare Cu (Figure S17).S8), probably due to the liquid nature of metallic Ga that leaches away from electrode surface into electrolyte under CO2RR conditions.Noteworthy, this near-surface Ga content is much higher than the bulky ratio as probed by energy dispersive spectroscopy (EDS), suggestive the surface enrichment of Ga in the Cu-Ga electrode (Figure S19).Noteworthy, this near-surface Ga content is much higher than the bulky ratio (Table S9) as probed by energy dispersive spectroscopy (EDS), suggestive the surface enrichment of Ga in the Cu-Ga electrode.S9).In line with the reference of Cu0.85Ga0.15ICS D#10-2892, both pristine and spent Cu-Ga electrodes share the same Fm-3m space group , reinforcing the alloying structure.The spent Cu-Ga electrode exhibits a smaller cell vol ume compared to that for pristine Cu-Ga, as due to the gradually leaching of metallic G a content during CO2RR and in good harmony with our previous XPS and EDS results.
Grazing-incidence X-ray diffraction (GI-XRD) at α = 0.5 o has been carried to better characterize the alloying structure for the Cu-Ga electrodes before and post CO2RR electrolysis.
As shown in Figure S20, two major peaks at 42.7 o and 49.8 o are noted for both the pristine and spent Cu-Ga electrodes deposited on glassy carbon substrates, corresponding to the (111) and (200) diffraction features of Cu0.85Ga0.15(ICSD#10-2892) alloy, respectively.Additionally, bo th pristine and spent Cu-Ga electrodes share the same Fm-3m space group, reinforcing th e alloying structure.The spent Cu-Ga electrode exhibits a smaller cell volume compared to that for pristine Cu-Ga (Table S9), as due to the gradually leaching of metallic Ga co ntent during CO2RR and in good harmony with our previous XPS and EDS results.The CO2RR performance was then screened in 0.1 M CO2-saturated CsHCO3 electrolyte for polished Cu and plated Cu-Ga, with the gaseous and the liquid products quantitively analyzed by online gas chromatography and 1 H nuclear magnetic response (NMR) spectroscopy, respectively.As plotted in Figure S21, similar steady-state current densities were noted on those two electrodes throughout the potential window of interest, however, the selectivity of products is quite different (Figure S22).A higher H2 Faradaic efficiency (FE) was observed on Cu over Cu-Ga at small overpotential regime, whereas at more negative potentials below -0.95 V, Cu electrode delivers a lower H2 FE but significantly higher hydrocarbon generation of CH4 and C2H4 compared to those on Cu-Ga.In contrast, Cu-Ga catalyst favors the liquid oxygenates generation including formate and ethanol.As shown in Figure S21b, Cu-Ga delivers a maximum formate FE of ~38.4% at -1.05 V vs. RHE, which is 4 times as high as that on bare Cu. Figure S21c plots the ratio of oxygenates to the sum of H2 and hydrocarbons as a function of cathodic potential, highlighting the promoted oxygenates selectivity on Cu-Ga alloy.
Moreover, Figure S21d depicts the potential dependence of oxygenates partial current density, in which Cu-Ga delivers ~7 times higher joxygenates than bare Cu at -1.15 V, experimentally confirming ML-predicted and DFT-calculated results.Considering the mass transport limitation of CO2 in aqueous solution, we did not continue to measure CO2RR products distribution below -1.2 V.Last but not least, we considered the potential surface hydrophilic effect on formate selectivity.As shown in Figure S23, a slightly lower contact angel was noted on Cu-Ga compared to bare Cu, suggesting an enhanced surface hydrophilicity of the former which may synergistically contribute to enhance the CO2-to-formate selectivity arisen from the surface hydride mechanism. 3,4 evertheless, the similar potential dependence of CO selectivity on Cu and Cu-Ga, as well as the difference in products distribution of hydrocarbons and ethanol highlights the dominant effect of Ga-doping in tuning CO2RR pathways.Given the EDLC of 0.048 mF/cm 2 for polished Cu foil (Figure S22), the roughness factor of Cu-Pd electrode is 12.16 times as high as that of polished Cu.S3.The equation of boundary conditions expressed using binding energy descriptors and the applied potentials.

Figure S2 .
Figure S2.Periodic table highlighting the elements used in this work.

Figure S7 .
Figure S7.The example of utilizing selectivity map.(a) All unique active motifs are scattered

Figure S10 .
Figure S10.ML-predicted product selectivity of pure Ag.At potentials more positive than −1.3

Figure S11 .
Figure S11.Productivity of (a) pure Cu and (b) masked Cu-Al without normalization.The

Figure S12 .
Figure S12.(a) Composition and (b) coordination number distribution of unique active motifs

Figure S13 .
Figure S13.Predicted binding energies of Cu-Ga alloy scattered on 2D selectivity map at two

Figure S15 .
Figure S15.Ex situ SEM image of Cu-Ga electrode (a) before and (b) after two hours CO2 electrolysis.In contrast to the relatively flat surface of polished Cu, the electroplated Cu-Ga electrode consists of densely packed nanoparticles.

Figure S16 .
Figure S16.(a) Ex situ SEM image of polished Cu foil, (b) enlarged version from panel (a).

Figure S17 .
Figure S17.Electrochemical double layer capacitance measurements for (a, b) polished Cu foil and (c, d) Cu-Ga electrode after CO2RR electrolysis at -0.95 V vs. RHE for 5400 s.The roughness factor of the Cu-Ga electrode is 2.67 times than the polished Cu electrode.

Figure S18 .
Figure S18.Comparison of the XPS spectra over Cu-Ga electrode before and after CO2RR electrolysis.(a) XPS survey spectra, (b) core-level Ga 3d spectra overlapped with O 2s, (c) Cu 2p and (d) LMM Auger spectra.Partially oxidized Cu surface species noted in the LMM spectra are probably arisen from the exposure to air during sample transfer process.

Figure S19 .
Figure S19.The EDS mapping and spectrum of Cu-Ga electrode after CO2RR electrolysis.(a) SEM image, (b) Mixing element, (c) Cu element, (d) Ga element, and (e) EDS spectrum. Figure

Figure S21 .
Figure S21.(a) Steady-state current densities, together with (b) the Faradaic efficiency of formate, (c) the selectivity ratio of oxygenates to the sum of H2 and hydrogenates, and (d) the partial current density of oxygenates on polished Cu versus plated Cu-Ga electrodes under different applied potentials.

Figure S23 .
Figure S23.The contact angel of (a) polished Cu and (b) plated Cu-Ga electrodes.Compared to bare Cu, the slightly lower contact angel on plated Cu-Ga electrode suggests its enhanced surface hydrophilicity.

Figure S24 .
Figure S24.Parity plot of bulk and active motif composition.The lighter color indicates a high density of points.

Figure S25 .
Figure S25.Composition-dependent C1+ productivity of Cu-Pd and formate productivity of Cu-Ga at -1.4 VRHE when compositions were derived from bulk structures.

Figure S28 .
Figure S28.Depth profile of TOF-SIMS measurement on as-prepared Cu-Pd electrode.Insert shows the overlapped distribution of Cu (reddish) and Pd (blue) components.

Figure S29 .
Figure S29.Comparison of the XPS spectra over Cu-Pd electrode before and after CO2RR electrolysis.(a) XPS survey spectra, (b) core-level Pd 3d spectra, (c) Cu 2p and (d) LMM Auger spectra.Partially oxidized Cu surface species noted in the LMM spectra probably arose from the exposure to air during sample transfer process.The near surface ratio of Cu:Pd slightly increases from 4:1 for pristine electrode to ~ 4.5:1 after CO2RR electrolysis, suggesting a relatively stable feature of Cu-Pd alloy.

Figure S31 .
Figure S31.SEM image and relevant EDS mapping for Cu-Pd electrode after CO2RR.(a) SEM image, (b) mixed element distribution, (c) Cu and (d) Pd element mapping, and (e) EDS spectrum.

Figure S32 .
Figure S32.Faradaic efficiency for CO2 electroreduction gas products at different applied potential for polished Cu and Cu-Pd electrodes.(a) CH4 (b) CO.

Figure S34 .
Figure S34.One and two-dimensional diagrams to illustrate how the maximum reaction energy

Figure S35 .
Figure S35.An example of uncertainty contribution for a point near the boundary condition in

Table S1 .
The mean absolute errors (eV) of each split of 5-fold cross validation.

Table S2 .
Top 20 active and selective elemental combination and corresponding productivity for each product.

Table S4 .
The atomic contents distribution in Cu-Pd electrode prior to and after electrolysis.

Table S5 .
The Gibbs free energy correction values for gaseous molecules.For all calculations, temperature was set to 298.15 K.

Table S6 .
The Gibbs free energy correction values for adsorbates.